Overview

Dataset statistics

Number of variables27
Number of observations203
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.4 KiB
Average record size in memory224.0 B

Variable types

Numeric17
Categorical10

Alerts

modele has a high cardinality: 141 distinct valuesHigh cardinality
car_ID is highly overall correlated with marqueHigh correlation
etat_de_route is highly overall correlated with empattement and 2 other fieldsHigh correlation
empattement is highly overall correlated with etat_de_route and 11 other fieldsHigh correlation
longueur_voiture is highly overall correlated with empattement and 9 other fieldsHigh correlation
largeur_voiture is highly overall correlated with empattement and 9 other fieldsHigh correlation
hauteur_voiture is highly overall correlated with etat_de_route and 3 other fieldsHigh correlation
poids_vehicule is highly overall correlated with empattement and 9 other fieldsHigh correlation
nombre_cylindres is highly overall correlated with poids_vehicule and 7 other fieldsHigh correlation
moteur_cc3 is highly overall correlated with empattement and 12 other fieldsHigh correlation
taux_alésage is highly overall correlated with empattement and 9 other fieldsHigh correlation
course is highly overall correlated with emplacement_moteur and 1 other fieldsHigh correlation
taux_compression is highly overall correlated with carburant and 2 other fieldsHigh correlation
chevaux is highly overall correlated with empattement and 11 other fieldsHigh correlation
tour_moteur is highly overall correlated with carburantHigh correlation
consommation_ville is highly overall correlated with longueur_voiture and 8 other fieldsHigh correlation
consommation_autoroute is highly overall correlated with empattement and 9 other fieldsHigh correlation
prix is highly overall correlated with empattement and 9 other fieldsHigh correlation
carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
turbo is highly overall correlated with taux_compression and 1 other fieldsHigh correlation
nombre_portes is highly overall correlated with etat_de_route and 2 other fieldsHigh correlation
type_vehicule is highly overall correlated with nombre_portesHigh correlation
roues_motrices is highly overall correlated with marqueHigh correlation
emplacement_moteur is highly overall correlated with empattement and 4 other fieldsHigh correlation
type_moteur is highly overall correlated with nombre_cylindres and 3 other fieldsHigh correlation
systeme_carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
marque is highly overall correlated with car_ID and 9 other fieldsHigh correlation
carburant is highly imbalanced (53.6%)Imbalance
emplacement_moteur is highly imbalanced (88.9%)Imbalance
car_ID is uniformly distributedUniform
modele is uniformly distributedUniform
car_ID has unique valuesUnique
etat_de_route has 66 (32.5%) zerosZeros

Reproduction

Analysis started2023-04-25 12:35:37.631346
Analysis finished2023-04-25 12:36:14.914162
Duration37.28 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

car_ID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct203
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.63054
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:15.033324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.1
Q151.5
median102
Q3154.5
95-th percentile194.9
Maximum205
Range204
Interquartile range (IQR)103

Descriptive statistics

Standard deviation59.497287
Coefficient of variation (CV)0.57972302
Kurtosis-1.204884
Mean102.63054
Median Absolute Deviation (MAD)52
Skewness0.016298887
Sum20834
Variance3539.9272
MonotonicityStrictly increasing
2023-04-25T14:36:15.185266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
141 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
Other values (193) 193
95.1%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%

etat_de_route
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83251232
Minimum-2
Maximum3
Zeros66
Zeros (%)32.5%
Negative25
Negative (%)12.3%
Memory size3.2 KiB
2023-04-25T14:36:15.310496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2473842
Coefficient of variation (CV)1.4983373
Kurtosis-0.67225171
Mean0.83251232
Median Absolute Deviation (MAD)1
Skewness0.2135015
Sum169
Variance1.5559674
MonotonicityNot monotonic
2023-04-25T14:36:15.411570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 66
32.5%
1 54
26.6%
2 31
15.3%
3 27
13.3%
-1 22
 
10.8%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.8%
0 66
32.5%
1 54
26.6%
2 31
15.3%
3 27
13.3%
ValueCountFrequency (%)
3 27
13.3%
2 31
15.3%
1 54
26.6%
0 66
32.5%
-1 22
 
10.8%
-2 3
 
1.5%

carburant
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
gas
183 
diesel
20 

Length

Max length6
Median length3
Mean length3.2955665
Min length3

Characters and Unicode

Total characters669
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 183
90.1%
diesel 20
 
9.9%

Length

2023-04-25T14:36:15.524486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:36:15.684831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
gas 183
90.1%
diesel 20
 
9.9%

Most occurring characters

ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 669
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 669
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

turbo
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
standard
166 
turbo
37 

Length

Max length8
Median length8
Mean length7.453202
Min length5

Characters and Unicode

Total characters1513
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstandard
2nd rowstandard
3rd rowstandard
4th rowstandard
5th rowstandard

Common Values

ValueCountFrequency (%)
standard 166
81.8%
turbo 37
 
18.2%

Length

2023-04-25T14:36:15.788610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:36:15.920789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
standard 166
81.8%
turbo 37
 
18.2%

Most occurring characters

ValueCountFrequency (%)
a 332
21.9%
d 332
21.9%
t 203
13.4%
r 203
13.4%
s 166
11.0%
n 166
11.0%
u 37
 
2.4%
b 37
 
2.4%
o 37
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1513
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 332
21.9%
d 332
21.9%
t 203
13.4%
r 203
13.4%
s 166
11.0%
n 166
11.0%
u 37
 
2.4%
b 37
 
2.4%
o 37
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1513
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 332
21.9%
d 332
21.9%
t 203
13.4%
r 203
13.4%
s 166
11.0%
n 166
11.0%
u 37
 
2.4%
b 37
 
2.4%
o 37
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 332
21.9%
d 332
21.9%
t 203
13.4%
r 203
13.4%
s 166
11.0%
n 166
11.0%
u 37
 
2.4%
b 37
 
2.4%
o 37
 
2.4%

nombre_portes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
4
114 
2
89 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters203
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 114
56.2%
2 89
43.8%

Length

2023-04-25T14:36:16.022265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:36:16.140458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 114
56.2%
2 89
43.8%

Most occurring characters

ValueCountFrequency (%)
4 114
56.2%
2 89
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 203
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 114
56.2%
2 89
43.8%

Most occurring scripts

ValueCountFrequency (%)
Common 203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 114
56.2%
2 89
43.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 114
56.2%
2 89
43.8%

type_vehicule
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
berline
95 
hayon
69 
break
25 
coupe
 
8
decapotable
 
6

Length

Max length11
Median length5
Mean length6.1133005
Min length5

Characters and Unicode

Total characters1241
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdecapotable
2nd rowdecapotable
3rd rowhayon
4th rowberline
5th rowberline

Common Values

ValueCountFrequency (%)
berline 95
46.8%
hayon 69
34.0%
break 25
 
12.3%
coupe 8
 
3.9%
decapotable 6
 
3.0%

Length

2023-04-25T14:36:16.243930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:36:16.386445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
berline 95
46.8%
hayon 69
34.0%
break 25
 
12.3%
coupe 8
 
3.9%
decapotable 6
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e 235
18.9%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
h 69
 
5.6%
y 69
 
5.6%
Other values (6) 73
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1241
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 235
18.9%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
h 69
 
5.6%
y 69
 
5.6%
Other values (6) 73
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1241
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 235
18.9%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
h 69
 
5.6%
y 69
 
5.6%
Other values (6) 73
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 235
18.9%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
h 69
 
5.6%
y 69
 
5.6%
Other values (6) 73
 
5.9%

roues_motrices
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
avant
118 
arriere
76 
4motrice
 
9

Length

Max length8
Median length5
Mean length5.8817734
Min length5

Characters and Unicode

Total characters1194
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarriere
2nd rowarriere
3rd rowarriere
4th rowavant
5th row4motrice

Common Values

ValueCountFrequency (%)
avant 118
58.1%
arriere 76
37.4%
4motrice 9
 
4.4%

Length

2023-04-25T14:36:16.513615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:36:16.650558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
avant 118
58.1%
arriere 76
37.4%
4motrice 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
a 312
26.1%
r 237
19.8%
e 161
13.5%
t 127
10.6%
v 118
 
9.9%
n 118
 
9.9%
i 85
 
7.1%
4 9
 
0.8%
m 9
 
0.8%
o 9
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1185
99.2%
Decimal Number 9
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 312
26.3%
r 237
20.0%
e 161
13.6%
t 127
10.7%
v 118
 
10.0%
n 118
 
10.0%
i 85
 
7.2%
m 9
 
0.8%
o 9
 
0.8%
c 9
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1185
99.2%
Common 9
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 312
26.3%
r 237
20.0%
e 161
13.6%
t 127
10.7%
v 118
 
10.0%
n 118
 
10.0%
i 85
 
7.2%
m 9
 
0.8%
o 9
 
0.8%
c 9
 
0.8%
Common
ValueCountFrequency (%)
4 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 312
26.1%
r 237
19.8%
e 161
13.5%
t 127
10.6%
v 118
 
9.9%
n 118
 
9.9%
i 85
 
7.1%
4 9
 
0.8%
m 9
 
0.8%
o 9
 
0.8%

emplacement_moteur
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
devant
200 
derrier
 
3

Length

Max length7
Median length6
Mean length6.0147783
Min length6

Characters and Unicode

Total characters1221
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdevant
2nd rowdevant
3rd rowdevant
4th rowdevant
5th rowdevant

Common Values

ValueCountFrequency (%)
devant 200
98.5%
derrier 3
 
1.5%

Length

2023-04-25T14:36:16.760640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:36:16.886673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
devant 200
98.5%
derrier 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 206
16.9%
d 203
16.6%
v 200
16.4%
a 200
16.4%
n 200
16.4%
t 200
16.4%
r 9
 
0.7%
i 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1221
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 206
16.9%
d 203
16.6%
v 200
16.4%
a 200
16.4%
n 200
16.4%
t 200
16.4%
r 9
 
0.7%
i 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1221
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 206
16.9%
d 203
16.6%
v 200
16.4%
a 200
16.4%
n 200
16.4%
t 200
16.4%
r 9
 
0.7%
i 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 206
16.9%
d 203
16.6%
v 200
16.4%
a 200
16.4%
n 200
16.4%
t 200
16.4%
r 9
 
0.7%
i 3
 
0.2%

empattement
Real number (ℝ)

Distinct37
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5089163
Minimum2.2
Maximum3.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:17.017154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile2.36
Q12.4
median2.46
Q32.6
95-th percentile2.79
Maximum3.07
Range0.87
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.15325772
Coefficient of variation (CV)0.061085227
Kurtosis0.99823752
Mean2.5089163
Median Absolute Deviation (MAD)0.07
Skewness1.0371195
Sum509.31
Variance0.023487929
MonotonicityNot monotonic
2023-04-25T14:36:17.148813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2.4 22
 
10.8%
2.38 19
 
9.4%
2.45 15
 
7.4%
2.43 13
 
6.4%
2.47 11
 
5.4%
2.44 8
 
3.9%
2.52 8
 
3.9%
2.65 8
 
3.9%
2.5 7
 
3.4%
2.74 7
 
3.4%
Other values (27) 85
41.9%
ValueCountFrequency (%)
2.2 2
 
1.0%
2.25 3
 
1.5%
2.27 3
 
1.5%
2.32 2
 
1.0%
2.36 6
 
3.0%
2.37 1
 
0.5%
2.38 19
9.4%
2.4 22
10.8%
2.42 5
 
2.5%
2.43 13
6.4%
ValueCountFrequency (%)
3.07 1
 
0.5%
2.94 2
 
1.0%
2.9 4
2.0%
2.87 2
 
1.0%
2.84 1
 
0.5%
2.79 3
1.5%
2.77 5
2.5%
2.74 7
3.4%
2.71 1
 
0.5%
2.69 3
1.5%

longueur_voiture
Real number (ℝ)

Distinct58
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4237438
Minimum3.58
Maximum5.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:17.298248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.58
5-th percentile4
Q14.23
median4.4
Q34.655
95-th percentile4.992
Maximum5.29
Range1.71
Interquartile range (IQR)0.425

Descriptive statistics

Standard deviation0.31344267
Coefficient of variation (CV)0.070854617
Kurtosis-0.072672474
Mean4.4237438
Median Absolute Deviation (MAD)0.18
Skewness0.14764323
Sum898.02
Variance0.09824631
MonotonicityNot monotonic
2023-04-25T14:36:17.453860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 15
 
7.4%
4.8 13
 
6.4%
4.74 13
 
6.4%
4.29 10
 
4.9%
4.46 9
 
4.4%
4.36 7
 
3.4%
4.49 7
 
3.4%
4.4 7
 
3.4%
4.22 7
 
3.4%
4.52 6
 
3.0%
Other values (48) 109
53.7%
ValueCountFrequency (%)
3.58 1
 
0.5%
3.67 2
 
1.0%
3.81 3
 
1.5%
3.96 3
 
1.5%
3.99 1
 
0.5%
4 15
7.4%
4.01 1
 
0.5%
4.03 4
 
2.0%
4.04 3
 
1.5%
4.05 1
 
0.5%
ValueCountFrequency (%)
5.29 1
 
0.5%
5.15 2
1.0%
5.07 2
1.0%
5.06 1
 
0.5%
5.05 4
2.0%
5 1
 
0.5%
4.92 1
 
0.5%
4.89 3
1.5%
4.87 1
 
0.5%
4.85 2
1.0%

largeur_voiture
Real number (ℝ)

Distinct24
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6752217
Minimum1.53
Maximum1.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:17.595100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.53
5-th percentile1.62
Q11.63
median1.66
Q31.7
95-th percentile1.79
Maximum1.84
Range0.31
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.054193686
Coefficient of variation (CV)0.032350158
Kurtosis0.71367564
Mean1.6752217
Median Absolute Deviation (MAD)0.03
Skewness0.89901287
Sum340.07
Variance0.0029369556
MonotonicityNot monotonic
2023-04-25T14:36:17.710624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.62 38
18.7%
1.66 29
14.3%
1.69 25
12.3%
1.63 17
8.4%
1.64 12
 
5.9%
1.68 10
 
4.9%
1.74 10
 
4.9%
1.67 10
 
4.9%
1.72 7
 
3.4%
1.65 7
 
3.4%
Other values (14) 38
18.7%
ValueCountFrequency (%)
1.53 1
 
0.5%
1.57 1
 
0.5%
1.59 1
 
0.5%
1.62 38
18.7%
1.63 17
8.4%
1.64 12
 
5.9%
1.65 7
 
3.4%
1.66 29
14.3%
1.67 10
 
4.9%
1.68 10
 
4.9%
ValueCountFrequency (%)
1.84 1
 
0.5%
1.83 1
 
0.5%
1.82 3
 
1.5%
1.81 3
 
1.5%
1.8 1
 
0.5%
1.79 5
2.5%
1.77 2
 
1.0%
1.75 5
2.5%
1.74 10
4.9%
1.73 3
 
1.5%

hauteur_voiture
Real number (ℝ)

Distinct26
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3639901
Minimum1.21
Maximum1.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:17.833955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.26
Q11.32
median1.37
Q31.41
95-th percentile1.46
Maximum1.52
Range0.31
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.061670956
Coefficient of variation (CV)0.045213638
Kurtosis-0.36651108
Mean1.3639901
Median Absolute Deviation (MAD)0.04
Skewness0.080395486
Sum276.89
Variance0.0038033068
MonotonicityNot monotonic
2023-04-25T14:36:17.948527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.41 23
 
11.3%
1.38 20
 
9.9%
1.29 19
 
9.4%
1.34 13
 
6.4%
1.35 12
 
5.9%
1.32 12
 
5.9%
1.37 12
 
5.9%
1.44 10
 
4.9%
1.31 9
 
4.4%
1.42 9
 
4.4%
Other values (16) 64
31.5%
ValueCountFrequency (%)
1.21 1
 
0.5%
1.24 2
 
1.0%
1.25 2
 
1.0%
1.26 7
 
3.4%
1.28 8
3.9%
1.29 19
9.4%
1.3 1
 
0.5%
1.31 9
4.4%
1.32 12
5.9%
1.33 3
 
1.5%
ValueCountFrequency (%)
1.52 2
 
1.0%
1.5 3
 
1.5%
1.49 4
 
2.0%
1.48 1
 
0.5%
1.46 3
 
1.5%
1.44 10
4.9%
1.43 5
 
2.5%
1.42 9
 
4.4%
1.41 23
11.3%
1.4 6
 
3.0%

poids_vehicule
Real number (ℝ)

Distinct79
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2800493
Minimum0.74
Maximum2.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:18.092413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile0.95
Q11.09
median1.21
Q31.475
95-th percentile1.75
Maximum2.03
Range1.29
Interquartile range (IQR)0.385

Descriptive statistics

Standard deviation0.26124132
Coefficient of variation (CV)0.20408693
Kurtosis-0.075467327
Mean1.2800493
Median Absolute Deviation (MAD)0.2
Skewness0.66259543
Sum259.85
Variance0.068247027
MonotonicityNot monotonic
2023-04-25T14:36:18.244434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.19 7
 
3.4%
1.14 7
 
3.4%
1.2 7
 
3.4%
1.27 6
 
3.0%
0.94 6
 
3.0%
1.01 5
 
2.5%
1.1 5
 
2.5%
0.99 5
 
2.5%
0.98 5
 
2.5%
1.15 5
 
2.5%
Other values (69) 145
71.4%
ValueCountFrequency (%)
0.74 1
 
0.5%
0.86 1
 
0.5%
0.91 1
 
0.5%
0.92 1
 
0.5%
0.94 6
3.0%
0.95 4
2.0%
0.96 3
1.5%
0.97 4
2.0%
0.98 5
2.5%
0.99 5
2.5%
ValueCountFrequency (%)
2.03 2
1.0%
1.98 1
0.5%
1.95 1
0.5%
1.88 2
1.0%
1.87 1
0.5%
1.86 1
0.5%
1.84 1
0.5%
1.76 1
0.5%
1.75 2
1.0%
1.74 1
0.5%

type_moteur
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
ACT
148 
soupapes en tête à manchon
 
13
soupapes en tête à flux croisés
 
13
double ACT
 
12
ligne des cylindres
 
12
Other values (2)
 
5

Length

Max length31
Median length3
Mean length7.955665
Min length3

Characters and Unicode

Total characters1615
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdouble ACT
2nd rowdouble ACT
3rd rowsoupapes en tête à manchon
4th rowACT
5th rowACT

Common Values

ValueCountFrequency (%)
ACT 148
72.9%
soupapes en tête à manchon 13
 
6.4%
soupapes en tête à flux croisés 13
 
6.4%
double ACT 12
 
5.9%
ligne des cylindres 12
 
5.9%
moteur rotatif 4
 
2.0%
double ACT à soupapes en V 1
 
0.5%

Length

2023-04-25T14:36:18.383793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:36:18.533149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
act 161
44.1%
soupapes 27
 
7.4%
en 27
 
7.4%
à 27
 
7.4%
tête 26
 
7.1%
manchon 13
 
3.6%
flux 13
 
3.6%
croisés 13
 
3.6%
double 13
 
3.6%
ligne 12
 
3.3%
Other values (5) 33
 
9.0%

Most occurring characters

ValueCountFrequency (%)
162
 
10.0%
C 161
 
10.0%
A 161
 
10.0%
T 161
 
10.0%
e 133
 
8.2%
s 104
 
6.4%
n 77
 
4.8%
o 74
 
4.6%
t 64
 
4.0%
u 57
 
3.5%
Other values (18) 461
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 969
60.0%
Uppercase Letter 484
30.0%
Space Separator 162
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 133
13.7%
s 104
 
10.7%
n 77
 
7.9%
o 74
 
7.6%
t 64
 
6.6%
u 57
 
5.9%
p 54
 
5.6%
l 50
 
5.2%
a 44
 
4.5%
i 41
 
4.2%
Other values (13) 271
28.0%
Uppercase Letter
ValueCountFrequency (%)
C 161
33.3%
A 161
33.3%
T 161
33.3%
V 1
 
0.2%
Space Separator
ValueCountFrequency (%)
162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1453
90.0%
Common 162
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 161
 
11.1%
A 161
 
11.1%
T 161
 
11.1%
e 133
 
9.2%
s 104
 
7.2%
n 77
 
5.3%
o 74
 
5.1%
t 64
 
4.4%
u 57
 
3.9%
p 54
 
3.7%
Other values (17) 407
28.0%
Common
ValueCountFrequency (%)
162
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549
95.9%
None 66
 
4.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
162
10.5%
C 161
10.4%
A 161
10.4%
T 161
10.4%
e 133
 
8.6%
s 104
 
6.7%
n 77
 
5.0%
o 74
 
4.8%
t 64
 
4.1%
u 57
 
3.7%
Other values (15) 395
25.5%
None
ValueCountFrequency (%)
à 27
40.9%
ê 26
39.4%
é 13
19.7%

nombre_cylindres
Real number (ℝ)

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3842365
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:18.658197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median4
Q34
95-th percentile6
Maximum12
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0855248
Coefficient of variation (CV)0.24759723
Kurtosis13.556088
Mean4.3842365
Median Absolute Deviation (MAD)0
Skewness2.7990217
Sum890
Variance1.1783641
MonotonicityNot monotonic
2023-04-25T14:36:18.757579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 157
77.3%
6 24
 
11.8%
5 11
 
5.4%
8 5
 
2.5%
2 4
 
2.0%
3 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
2 4
 
2.0%
3 1
 
0.5%
4 157
77.3%
5 11
 
5.4%
6 24
 
11.8%
8 5
 
2.5%
12 1
 
0.5%
ValueCountFrequency (%)
12 1
 
0.5%
8 5
 
2.5%
6 24
 
11.8%
5 11
 
5.4%
4 157
77.3%
3 1
 
0.5%
2 4
 
2.0%

moteur_cc3
Real number (ℝ)

Distinct44
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2083.6749
Minimum1000
Maximum5342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:18.887950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1475
Q11598
median1966
Q32343.5
95-th percentile3312.2
Maximum5342
Range4342
Interquartile range (IQR)745.5

Descriptive statistics

Standard deviation684.49756
Coefficient of variation (CV)0.32850497
Kurtosis5.2393703
Mean2083.6749
Median Absolute Deviation (MAD)376
Skewness1.9339231
Sum422986
Variance468536.91
MonotonicityNot monotonic
2023-04-25T14:36:19.024145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1999 15
 
7.4%
1508 15
 
7.4%
1606 14
 
6.9%
1590 13
 
6.4%
1475 12
 
5.9%
1770 12
 
5.9%
1803 12
 
5.9%
1786 8
 
3.9%
1966 7
 
3.4%
2311 7
 
3.4%
Other values (34) 88
43.3%
ValueCountFrequency (%)
1000 1
 
0.5%
1147 3
 
1.5%
1295 1
 
0.5%
1311 1
 
0.5%
1475 12
5.9%
1491 5
 
2.5%
1508 15
7.4%
1590 13
6.4%
1606 14
6.9%
1688 1
 
0.5%
ValueCountFrequency (%)
5342 1
 
0.5%
5047 1
 
0.5%
4982 1
 
0.5%
4228 2
 
1.0%
3835 2
 
1.0%
3425 3
1.5%
3327 1
 
0.5%
3179 3
1.5%
2999 4
2.0%
2966 6
3.0%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
injection multipoint multipoint port unique
94 
carburateur 2 corps
64 
injection indirecte
20 
carburateur 1 corps
11 
injection D monopoint
 
9
Other values (3)
 
5

Length

Max length43
Median length30
Mean length30.256158
Min length19

Characters and Unicode

Total characters6142
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowinjection multipoint multipoint port unique
2nd rowinjection multipoint multipoint port unique
3rd rowinjection multipoint multipoint port unique
4th rowinjection multipoint multipoint port unique
5th rowinjection multipoint multipoint port unique

Common Values

ValueCountFrequency (%)
injection multipoint multipoint port unique 94
46.3%
carburateur 2 corps 64
31.5%
injection indirecte 20
 
9.9%
carburateur 1 corps 11
 
5.4%
injection D monopoint 9
 
4.4%
carburateur 4 corps 3
 
1.5%
injection carburant multipoint 1
 
0.5%
injection monopoint 1
 
0.5%

Length

2023-04-25T14:36:19.146080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:36:19.293667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
multipoint 189
24.4%
injection 125
16.1%
port 94
12.1%
unique 94
12.1%
carburateur 78
10.1%
corps 78
10.1%
2 64
 
8.2%
indirecte 20
 
2.6%
1 11
 
1.4%
monopoint 10
 
1.3%
Other values (3) 13
 
1.7%

Most occurring characters

ValueCountFrequency (%)
i 772
12.6%
t 706
11.5%
n 574
9.3%
573
9.3%
u 534
8.7%
o 516
8.4%
r 428
7.0%
p 371
 
6.0%
e 337
 
5.5%
c 302
 
4.9%
Other values (12) 1029
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5482
89.3%
Space Separator 573
 
9.3%
Decimal Number 78
 
1.3%
Uppercase Letter 9
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 772
14.1%
t 706
12.9%
n 574
10.5%
u 534
9.7%
o 516
9.4%
r 428
7.8%
p 371
6.8%
e 337
6.1%
c 302
 
5.5%
m 199
 
3.6%
Other values (7) 743
13.6%
Decimal Number
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%
Space Separator
ValueCountFrequency (%)
573
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5491
89.4%
Common 651
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 772
14.1%
t 706
12.9%
n 574
10.5%
u 534
9.7%
o 516
9.4%
r 428
7.8%
p 371
6.8%
e 337
6.1%
c 302
 
5.5%
m 199
 
3.6%
Other values (8) 752
13.7%
Common
ValueCountFrequency (%)
573
88.0%
2 64
 
9.8%
1 11
 
1.7%
4 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 772
12.6%
t 706
11.5%
n 574
9.3%
573
9.3%
u 534
8.7%
o 516
8.4%
r 428
7.0%
p 371
 
6.0%
e 337
 
5.5%
c 302
 
4.9%
Other values (12) 1029
16.8%

taux_alésage
Real number (ℝ)

Distinct38
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3268966
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:19.461347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27062949
Coefficient of variation (CV)0.081345929
Kurtosis-0.76767189
Mean3.3268966
Median Absolute Deviation (MAD)0.23
Skewness0.040086683
Sum675.36
Variance0.073240321
MonotonicityNot monotonic
2023-04-25T14:36:19.596322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 21
 
10.3%
3.19 20
 
9.9%
3.15 15
 
7.4%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.43 8
 
3.9%
3.78 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 83
40.9%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.5%
3.03 12
5.9%
3.05 6
3.0%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.5%
3.63 2
 
1.0%
3.62 21
10.3%
3.61 1
 
0.5%
3.6 1
 
0.5%

course
Real number (ℝ)

Distinct36
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2628571
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:19.742461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.30564202
Coefficient of variation (CV)0.093673123
Kurtosis2.4002381
Mean3.2628571
Median Absolute Deviation (MAD)0.14
Skewness-0.65968543
Sum662.36
Variance0.093417044
MonotonicityNot monotonic
2023-04-25T14:36:19.869435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.4 20
 
9.9%
3.15 14
 
6.9%
3.03 14
 
6.9%
3.23 14
 
6.9%
3.39 13
 
6.4%
2.64 10
 
4.9%
3.35 9
 
4.4%
3.29 9
 
4.4%
3.46 8
 
3.9%
3.07 6
 
3.0%
Other values (26) 86
42.4%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.64 10
4.9%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.9%
3.07 6
3.0%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.5%
3.58 6
3.0%
3.54 4
2.0%
3.52 5
2.5%
3.5 6
3.0%
3.47 4
2.0%
3.46 8
3.9%

taux_compression
Real number (ℝ)

Distinct32
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.15133
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:19.994888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.55
median9
Q39.4
95-th percentile21.86
Maximum23
Range16
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation3.9905801
Coefficient of variation (CV)0.39310909
Kurtosis5.1392945
Mean10.15133
Median Absolute Deviation (MAD)0.4
Skewness2.5938477
Sum2060.72
Variance15.924729
MonotonicityNot monotonic
2023-04-25T14:36:20.120016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 45
22.2%
9.4 26
12.8%
8.5 14
 
6.9%
9.5 12
 
5.9%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.5%
Other values (22) 58
28.6%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.5%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.5%
8.5 14
6.9%
ValueCountFrequency (%)
23 5
2.5%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.5%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

chevaux
Real number (ℝ)

Distinct59
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.39901
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:20.270356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile181.4
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.631013
Coefficient of variation (CV)0.37961099
Kurtosis2.6470236
Mean104.39901
Median Absolute Deviation (MAD)25
Skewness1.3928436
Sum21193
Variance1570.6172
MonotonicityNot monotonic
2023-04-25T14:36:20.625715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.4%
70 11
 
5.4%
116 9
 
4.4%
69 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
3.0%
160 6
 
3.0%
101 6
 
3.0%
62 6
 
3.0%
Other values (49) 116
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
3.0%
64 1
 
0.5%
68 19
9.4%
69 9
4.4%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

tour_moteur
Real number (ℝ)

Distinct22
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5127.8325
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:20.753770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5990
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation478.5252
Coefficient of variation (CV)0.093319195
Kurtosis0.074796639
Mean5127.8325
Median Absolute Deviation (MAD)300
Skewness0.060106102
Sum1040950
Variance228986.37
MonotonicityNot monotonic
2023-04-25T14:36:20.869121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5500 37
18.2%
4800 35
17.2%
5000 27
13.3%
5200 23
11.3%
5400 13
 
6.4%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.5%
Other values (12) 33
16.3%
ValueCountFrequency (%)
4150 5
 
2.5%
4200 5
 
2.5%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 35
17.2%
5000 27
13.3%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.2%
5400 13
 
6.4%
5300 1
 
0.5%
5250 7
 
3.4%

consommation_ville
Real number (ℝ)

Distinct28
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9831385
Minimum4.8002976
Maximum18.093429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:20.982926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.8002976
5-th percentile6.3571509
Q17.8404861
median9.8006076
Q312.379715
95-th percentile14.700911
Maximum18.093429
Range13.293132
Interquartile range (IQR)4.5392288

Descriptive statistics

Standard deviation2.5761447
Coefficient of variation (CV)0.25804958
Kurtosis-0.17496577
Mean9.9831385
Median Absolute Deviation (MAD)2.2130404
Skewness0.5511603
Sum2026.5771
Variance6.6365213
MonotonicityNot monotonic
2023-04-25T14:36:21.122468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7.587567194 27
13.3%
12.37971489 27
13.3%
9.800607625 22
10.8%
8.711651222 14
 
6.9%
13.83615194 13
 
6.4%
9.046714731 12
 
5.9%
10.226721 12
 
5.9%
11.20069443 8
 
3.9%
9.40858332 8
 
3.9%
7.8404861 8
 
3.9%
Other values (18) 52
25.6%
ValueCountFrequency (%)
4.800297612 1
 
0.5%
5.004565596 1
 
0.5%
5.226990733 1
 
0.5%
6.189857447 7
 
3.4%
6.357150892 6
 
3.0%
6.533738417 1
 
0.5%
6.720416657 1
 
0.5%
6.918075971 1
 
0.5%
7.127714636 1
 
0.5%
7.587567194 27
13.3%
ValueCountFrequency (%)
18.09342946 1
 
0.5%
16.80104164 2
 
1.0%
15.6809722 3
 
1.5%
14.70091144 6
 
3.0%
13.83615194 13
6.4%
13.06747683 3
 
1.5%
12.37971489 27
13.3%
11.76072915 3
 
1.5%
11.20069443 8
 
3.9%
10.69157195 4
 
2.0%

consommation_autoroute
Real number (ℝ)

Distinct30
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0574103
Minimum4.3558256
Maximum14.700911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:21.257440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.3558256
5-th percentile5.4831306
Q16.918076
median7.8404861
Q39.4085833
95-th percentile10.691572
Maximum14.700911
Range10.345086
Interquartile range (IQR)2.4905073

Descriptive statistics

Standard deviation1.8537351
Coefficient of variation (CV)0.23006586
Kurtosis1.1338208
Mean8.0574103
Median Absolute Deviation (MAD)1.4833352
Skewness0.81151544
Sum1635.6543
Variance3.4363337
MonotonicityNot monotonic
2023-04-25T14:36:21.382481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9.40858332 19
 
9.4%
6.189857447 17
 
8.4%
9.800607625 17
 
8.4%
7.8404861 16
 
7.9%
7.350455719 16
 
7.9%
6.918075971 14
 
6.9%
8.400520821 13
 
6.4%
6.357150892 12
 
5.9%
8.11084769 10
 
4.9%
7.127714636 9
 
4.4%
Other values (20) 60
29.6%
ValueCountFrequency (%)
4.355825611 1
 
0.5%
4.438011 1
 
0.5%
4.70429166 1
 
0.5%
5.004565596 2
 
1.0%
5.1133605 2
 
1.0%
5.470106581 4
 
2.0%
5.600347214 3
 
1.5%
5.736941049 3
 
1.5%
6.031143154 2
 
1.0%
6.189857447 17
8.4%
ValueCountFrequency (%)
14.70091144 2
 
1.0%
13.83615194 1
 
0.5%
13.06747683 2
 
1.0%
12.37971489 2
 
1.0%
11.76072915 2
 
1.0%
10.69157195 8
3.9%
10.226721 7
 
3.4%
9.800607625 17
8.4%
9.40858332 19
9.4%
9.046714731 3
 
1.5%

prix
Real number (ℝ)

Distinct187
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13347.2
Minimum5151
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-25T14:36:21.526651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5151
5-th percentile6229
Q17847
median10345
Q316509
95-th percentile32500.2
Maximum45400
Range40249
Interquartile range (IQR)8662

Descriptive statistics

Standard deviation7995.7399
Coefficient of variation (CV)0.59905745
Kurtosis3.0206407
Mean13347.2
Median Absolute Deviation (MAD)3300
Skewness1.7716934
Sum2709481.7
Variance63931856
MonotonicityNot monotonic
2023-04-25T14:36:21.673825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
18150 2
 
1.0%
7898 2
 
1.0%
8916.5 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (177) 183
90.1%
ValueCountFrequency (%)
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
6229 2
1.0%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

marque
Categorical

Distinct28
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
toyota
31 
nissan
17 
mazda
15 
mitsubishi
13 
honda
13 
Other values (23)
114 

Length

Max length11
Median length10
Mean length6.1477833
Min length2

Characters and Unicode

Total characters1248
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 31
15.3%
nissan 17
 
8.4%
mazda 15
 
7.4%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
peugeot 11
 
5.4%
volvo 11
 
5.4%
subaru 10
 
4.9%
volkswagen 9
 
4.4%
dodge 9
 
4.4%
Other values (18) 64
31.5%

Length

2023-04-25T14:36:21.817802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 31
15.3%
nissan 18
 
8.9%
mazda 15
 
7.4%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
peugeot 11
 
5.4%
volvo 11
 
5.4%
subaru 10
 
4.9%
volkswagen 9
 
4.4%
dodge 9
 
4.4%
Other values (17) 63
31.0%

Most occurring characters

ValueCountFrequency (%)
o 150
12.0%
a 150
12.0%
t 100
 
8.0%
s 97
 
7.8%
u 81
 
6.5%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.6%
d 55
 
4.4%
m 49
 
3.9%
Other values (17) 372
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1244
99.7%
Dash Punctuation 3
 
0.2%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 150
12.1%
a 150
12.1%
t 100
 
8.0%
s 97
 
7.8%
u 81
 
6.5%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.7%
d 55
 
4.4%
m 49
 
3.9%
Other values (15) 368
29.6%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1245
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 150
12.0%
a 150
12.0%
t 100
 
8.0%
s 97
 
7.8%
u 81
 
6.5%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.7%
d 55
 
4.4%
m 49
 
3.9%
Other values (16) 369
29.6%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 150
12.0%
a 150
12.0%
t 100
 
8.0%
s 97
 
7.8%
u 81
 
6.5%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.6%
d 55
 
4.4%
m 49
 
3.9%
Other values (17) 372
29.8%

modele
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct141
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
corolla
 
6
corona
 
6
504
 
6
dl
 
4
civic
 
3
Other values (136)
178 

Length

Max length25
Median length18
Mean length7.0788177
Min length2

Characters and Unicode

Total characters1437
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)50.2%

Sample

1st rowgiulia
2nd rowstelvio
3rd rowQuadrifoglio
4th row100 ls
5th row100ls

Common Values

ValueCountFrequency (%)
corolla 6
 
3.0%
corona 6
 
3.0%
504 6
 
3.0%
dl 4
 
2.0%
civic 3
 
1.5%
mirage g4 3
 
1.5%
mark ii 3
 
1.5%
g4 3
 
1.5%
rabbit 3
 
1.5%
outlander 3
 
1.5%
Other values (131) 163
80.3%

Length

2023-04-25T14:36:21.945264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
corolla 12
 
4.2%
sw 10
 
3.5%
corona 9
 
3.2%
glc 8
 
2.8%
custom 8
 
2.8%
civic 8
 
2.8%
504 7
 
2.5%
g4 6
 
2.1%
deluxe 5
 
1.8%
mirage 4
 
1.4%
Other values (141) 206
72.8%

Most occurring characters

ValueCountFrequency (%)
c 108
 
7.5%
a 107
 
7.4%
l 103
 
7.2%
r 100
 
7.0%
e 100
 
7.0%
o 93
 
6.5%
82
 
5.7%
i 71
 
4.9%
t 67
 
4.7%
s 54
 
3.8%
Other values (35) 552
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1128
78.5%
Decimal Number 179
 
12.5%
Space Separator 82
 
5.7%
Close Punctuation 13
 
0.9%
Open Punctuation 13
 
0.9%
Uppercase Letter 12
 
0.8%
Dash Punctuation 10
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 108
 
9.6%
a 107
 
9.5%
l 103
 
9.1%
r 100
 
8.9%
e 100
 
8.9%
o 93
 
8.2%
i 71
 
6.3%
t 67
 
5.9%
s 54
 
4.8%
u 41
 
3.6%
Other values (15) 284
25.2%
Decimal Number
ValueCountFrequency (%)
0 44
24.6%
4 37
20.7%
1 23
12.8%
2 21
11.7%
5 18
10.1%
9 12
 
6.7%
6 12
 
6.7%
3 10
 
5.6%
7 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M 4
33.3%
D 3
25.0%
U 1
 
8.3%
X 1
 
8.3%
Q 1
 
8.3%
V 1
 
8.3%
C 1
 
8.3%
Space Separator
ValueCountFrequency (%)
82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140
79.3%
Common 297
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 108
 
9.5%
a 107
 
9.4%
l 103
 
9.0%
r 100
 
8.8%
e 100
 
8.8%
o 93
 
8.2%
i 71
 
6.2%
t 67
 
5.9%
s 54
 
4.7%
u 41
 
3.6%
Other values (22) 296
26.0%
Common
ValueCountFrequency (%)
82
27.6%
0 44
14.8%
4 37
12.5%
1 23
 
7.7%
2 21
 
7.1%
5 18
 
6.1%
) 13
 
4.4%
( 13
 
4.4%
9 12
 
4.0%
6 12
 
4.0%
Other values (3) 22
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 108
 
7.5%
a 107
 
7.4%
l 103
 
7.2%
r 100
 
7.0%
e 100
 
7.0%
o 93
 
6.5%
82
 
5.7%
i 71
 
4.9%
t 67
 
4.7%
s 54
 
3.8%
Other values (35) 552
38.4%

Interactions

2023-04-25T14:36:11.867132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:40.101279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:46.096436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:50.018508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:52.036089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:53.969006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:57.660783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:59.791317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:48.212567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:50.146364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:52.151038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:36:09.945986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:12.112569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:40.362044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:54.191084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:36:01.895114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:04.294602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:06.117662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:08.114853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:10.063686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:12.229569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:40.471950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:42.491127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:44.513381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:46.453590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:56.117522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:58.021391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:00.092903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:02.011512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:04.392252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:36:08.220051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:36:12.371342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:40.600398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:46.580715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:48.539388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:52.486720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:58.156267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:00.202627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:36:00.422933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:36:06.547268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:36:10.552228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:12.781788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:35:56.995306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:59.110873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:00.950155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:03.279866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:05.272259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:07.249837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:09.122102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:11.128493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:13.386427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:41.564008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:43.489713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:45.523245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:47.520871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:49.449374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:51.325109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:53.393120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:55.270911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:57.121968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:59.232780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:01.065906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:03.456948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:05.386778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:07.355030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:09.237092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:11.266807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:13.500297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:41.679403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:43.594285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:45.635369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:47.632641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:49.557026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:51.602024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:53.502872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:55.374168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:57.225276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:59.335753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:01.175903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:03.570440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:05.483644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:07.458976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:09.341371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:11.388984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:13.612968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:41.795041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:43.693892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:45.745561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:47.744587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:49.663286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:51.700144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:53.606560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:55.473004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:57.323068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:59.440078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:01.275626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:03.670284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:05.581185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:07.556486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:09.448353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:11.500190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:13.731883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:41.917117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:43.809070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:45.859392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:47.860424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:49.781808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:51.805284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:53.722276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:55.582180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:57.437008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:59.554561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:01.390078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:03.811842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:05.687214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:07.655912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:09.568370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:11.624173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:13.851226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:42.030330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:43.920302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:45.966963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:47.969193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:49.898185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:51.909766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:53.834725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:55.689033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:57.541389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:35:59.660012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:01.517717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:03.959537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:05.789150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:07.767879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:09.684582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:36:11.738763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-25T14:36:22.091518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
car_IDetat_de_routeempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculenombre_cylindresmoteur_cc3taux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixcarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurtype_moteursysteme_carburantmarque
car_ID1.000-0.1570.2010.1650.1570.2640.132-0.1160.0940.269-0.1510.1490.012-0.224-0.047-0.0160.0300.2870.2600.3460.1790.4240.3320.4060.3860.799
etat_de_route-0.1571.000-0.538-0.390-0.270-0.539-0.258-0.143-0.175-0.173-0.0160.029-0.0060.2830.019-0.053-0.1430.2190.1810.6820.3340.2660.2710.2210.2690.445
empattement0.201-0.5381.0000.9100.8230.6440.7670.3610.6470.5550.220-0.1360.503-0.3200.4940.5390.6800.3450.3080.4270.3360.3870.5680.3370.2220.503
longueur_voiture0.165-0.3900.9101.0000.8930.5390.8890.4670.7810.6570.174-0.1940.657-0.2740.6690.6970.8030.1040.2060.3620.2390.4090.0000.3150.3260.490
largeur_voiture0.157-0.2700.8230.8931.0000.3660.8730.4700.7740.6400.215-0.1560.689-0.2140.6890.7050.8170.2310.3000.3040.1240.4020.1590.3680.2460.515
hauteur_voiture0.264-0.5390.6440.5390.3661.0000.3550.0900.2040.233-0.029-0.0010.017-0.3000.0730.1400.2530.2750.2450.5430.4920.3640.2600.3820.2970.468
poids_vehicule0.132-0.2580.7670.8890.8730.3551.0000.5710.8770.7210.150-0.2140.806-0.2450.8100.8310.9090.3130.3710.2660.2380.4530.1000.3220.2950.480
nombre_cylindres-0.116-0.1430.3610.4670.4700.0900.5711.0000.6940.2180.062-0.1350.578-0.0960.5150.5100.5880.1690.2080.1520.0970.3290.2960.5540.3730.545
moteur_cc30.094-0.1750.6470.7810.7740.2040.8770.6941.0000.7160.284-0.2330.815-0.2810.7290.7190.8260.1550.2680.2080.2010.4670.6180.5270.3310.513
taux_alésage0.269-0.1730.5550.6570.6400.2330.7210.2180.7161.000-0.065-0.1700.655-0.2940.6330.6320.6720.1670.3460.1640.1540.4410.3260.4120.3480.528
course-0.151-0.0160.2200.1740.215-0.0290.1500.0620.284-0.0651.000-0.0660.117-0.0870.0100.0140.0900.3800.2710.1200.1650.3560.6180.4040.3080.604
taux_compression0.1490.029-0.136-0.194-0.156-0.001-0.214-0.135-0.233-0.170-0.0661.000-0.353-0.015-0.477-0.443-0.1720.9930.5530.1870.0470.1110.0000.3360.5180.483
chevaux0.012-0.0060.5030.6570.6890.0170.8060.5780.8150.6550.117-0.3531.0000.1070.9110.8840.8540.2210.3410.1640.1880.4000.8430.5180.3170.453
tour_moteur-0.2240.283-0.320-0.274-0.214-0.300-0.245-0.096-0.281-0.294-0.087-0.0150.1071.0000.1220.050-0.0770.5940.3100.2390.0710.2460.4470.3620.3630.463
consommation_ville-0.0470.0190.4940.6690.6890.0730.8100.5150.7290.6330.010-0.4770.9110.1221.0000.9680.8280.3120.1860.1190.0750.3860.3630.3380.3160.385
consommation_autoroute-0.016-0.0530.5390.6970.7050.1400.8310.5100.7190.6320.014-0.4430.8840.0500.9681.0000.8220.3610.2980.1700.1790.4340.2310.3450.3360.374
prix0.030-0.1430.6800.8030.8170.2530.9090.5880.8260.6720.090-0.1720.854-0.0770.8280.8221.0000.3290.4040.0000.2290.4440.4500.2850.2870.369
carburant0.2870.2190.3450.1040.2310.2750.3130.1690.1550.1670.3800.9930.2210.5940.3120.3610.3291.0000.3730.1610.1740.0850.0000.2470.9850.381
turbo0.2600.1810.3080.2060.3000.2450.3710.2080.2680.3460.2710.5530.3410.3100.1860.2980.4040.3731.0000.0000.0000.1140.0000.1460.6090.377
nombre_portes0.3460.6820.4270.3620.3040.5430.2660.1520.2080.1640.1200.1870.1640.2390.1190.1700.0000.1610.0001.0000.7390.0510.0680.2020.2460.344
type_vehicule0.1790.3340.3360.2390.1240.4920.2380.0970.2010.1540.1650.0470.1880.0710.0750.1790.2290.1740.0000.7391.0000.2120.4380.1450.1440.356
roues_motrices0.4240.2660.3870.4090.4020.3640.4530.3290.4670.4410.3560.1110.4000.2460.3860.4340.4440.0850.1140.0510.2121.0000.1230.4420.3850.602
emplacement_moteur0.3320.2710.5680.0000.1590.2600.1000.2960.6180.3260.6180.0000.8430.4470.3630.2310.4500.0000.0000.0680.4380.1231.0000.4360.0000.728
type_moteur0.4060.2210.3370.3150.3680.3820.3220.5540.5270.4120.4040.3360.5180.3620.3380.3450.2850.2470.1460.2020.1450.4420.4361.0000.3750.625
systeme_carburant0.3860.2690.2220.3260.2460.2970.2950.3730.3310.3480.3080.5180.3170.3630.3160.3360.2870.9850.6090.2460.1440.3850.0000.3751.0000.490
marque0.7990.4450.5030.4900.5150.4680.4800.5450.5130.5280.6040.4830.4530.4630.3850.3740.3690.3810.3770.3440.3560.6020.7280.6250.4901.000

Missing values

2023-04-25T14:36:14.091395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-25T14:36:14.728810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

car_IDetat_de_routecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetype_moteurnombre_cylindresmoteur_cc3systeme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarquemodele
013gasstandard2decapotablearrieredevant2.254.291.631.241.27double ACT42130.0injection multipoint multipoint port unique3.472.689.0111500011.2006948.71165113495.000alfa-romerogiulia
123gasstandard2decapotablearrieredevant2.254.291.631.241.27double ACT42130.0injection multipoint multipoint port unique3.472.689.0111500011.2006948.71165116500.000alfa-romerostelvio
231gasstandard2hayonarrieredevant2.404.351.661.331.41soupapes en tête à manchon62491.0injection multipoint multipoint port unique2.683.479.0154500012.3797159.04671516500.000alfa-romeroQuadrifoglio
342gasstandard4berlineavantdevant2.534.491.681.381.17ACT41786.0injection multipoint multipoint port unique3.193.4010.010255009.8006087.84048613950.000audi100 ls
452gasstandard4berline4motricedevant2.524.491.691.381.41ACT52229.0injection multipoint multipoint port unique3.193.408.0115550013.06747710.69157217450.000audi100ls
562gasstandard2berlineavantdevant2.534.501.681.351.25ACT52229.0injection multipoint multipoint port unique3.193.408.5110550012.3797159.40858315250.000audifox
671gasstandard4berlineavantdevant2.694.891.811.411.42ACT52229.0injection multipoint multipoint port unique3.193.408.5110550012.3797159.40858317710.000audi100ls
781gasstandard4breakavantdevant2.694.891.811.411.48ACT52229.0injection multipoint multipoint port unique3.193.408.5110550012.3797159.40858318920.000audi5000
891gasturbo4berlineavantdevant2.694.891.811.421.54ACT52147.0injection multipoint multipoint port unique3.133.408.3140550013.83615211.76072923875.000audi4000
9100gasturbo2hayon4motricedevant2.534.531.721.321.53ACT52147.0injection multipoint multipoint port unique3.133.407.0160550014.70091110.69157217859.167audi5000s (diesel)
car_IDetat_de_routecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetype_moteurnombre_cylindresmoteur_cc3systeme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarquemodele
195196-1gasstandard4breakarrieredevant2.654.81.711.461.52ACT42311.0injection multipoint multipoint port unique3.783.159.5114540010.2267218.40052113415.0volvo144ea
196197-2gasstandard4berlinearrieredevant2.654.81.711.431.47ACT42311.0injection multipoint multipoint port unique3.783.159.511454009.8006088.40052115985.0volvo244dl
197198-1gasstandard4breakarrieredevant2.654.81.711.461.52ACT42311.0injection multipoint multipoint port unique3.783.159.511454009.8006088.40052116515.0volvo245
198199-2gasturbo4berlinearrieredevant2.654.81.711.431.52ACT42130.0injection multipoint multipoint port unique3.623.157.5162510013.83615210.69157218420.0volvo264gl
199200-1gasturbo4breakarrieredevant2.654.81.711.461.58ACT42130.0injection multipoint multipoint port unique3.623.157.5162510013.83615210.69157218950.0volvodiesel
200201-1gasstandard4berlinearrieredevant2.774.81.751.411.48ACT42311.0injection multipoint multipoint port unique3.783.159.5114540010.2267218.40052116845.0volvo145e (sw)
201202-1gasturbo4berlinearrieredevant2.774.81.751.411.52ACT42311.0injection multipoint multipoint port unique3.783.158.7160530012.3797159.40858319045.0volvo144ea
202203-1gasstandard4berlinearrieredevant2.774.81.751.411.51soupapes en tête à manchon62835.0injection multipoint multipoint port unique3.582.878.8134550013.06747710.22672121485.0volvo244dl
203204-1dieselturbo4berlinearrieredevant2.774.81.751.411.61ACT62376.0injection indirecte3.013.4023.010648009.0467158.71165122470.0volvo246
204205-1gasturbo4berlinearrieredevant2.774.81.751.411.53ACT42311.0injection multipoint multipoint port unique3.783.159.5114540012.3797159.40858322625.0volvo264gl